1 About

Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

2 Citation

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom

3 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

4 Emissions Levy Case Study

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy value.

NB: no maps in the interests of speed

4.1 Data

We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

All analysis is at LSOA level. Cautions on inference from area level data apply.

4.2 CREDS place-based emissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

  • Emissions are presented as per capita…
  • Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
##        region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: South East    148          7334.257        2838.428

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

##            LSOA11NM     WD18NM nGasMeters nElecMeters epc_total
## 1: Southampton 005F Swaythling        881         976       719
## 2: Southampton 020D Freemantle        869        1139       881
## 3: Southampton 026A    Sholing        846         908       425
## 4: Southampton 021A Freemantle        750         914       555
## 5: Southampton 030C    Sholing        724         769       393
## 6: Southampton 019D  Millbrook        718         742       356
##            LSOA11NM     WD18NM nGasMeters nElecMeters epc_total
## 1: Southampton 031D   Woolston        560        1392      1140
## 2: Southampton 029C    Bargate        414        1379      1100
## 3: Southampton 023D    Bargate        342        1341      1080
## 4: Southampton 029G    Bargate        407        1258      1130
## 5: Southampton 020D Freemantle        869        1139       881
## 6: Southampton 029A    Bargate        586        1105       794
##            LSOA11NM     WD18NM nGasMeters nElecMeters epc_total
## 1: Southampton 005F Swaythling        881         976       719
## 2: Southampton 020D Freemantle        869        1139       881
## 3: Southampton 026A    Sholing        846         908       425
## 4: Southampton 021A Freemantle        750         914       555
## 5: Southampton 030C    Sholing        724         769       393
## 6: Southampton 019D  Millbrook        718         742       356

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.

Check that the assumption seems sensible…

Check for outliers - what might this indicate?

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values
Table 4.1: Data summary
Name …[]
Number of rows 148
Number of columns 9
Key NULL
_______________________
Column type frequency:
numeric 9
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
CREDStotal_kgco2e_pdw 0 1 17126.66 7014.73 5845.64 11905.74 15464.94 21050.67 44958.42 ▆▇▃▁▁
CREDSgas_kgco2e2018_pdw 0 1 1758.06 626.82 12.82 1358.01 1727.03 2166.35 3659.41 ▁▅▇▃▁
CREDSelec_kgco2e2018_pdw 0 1 1004.41 109.16 655.50 938.64 988.98 1044.12 1459.95 ▁▆▇▂▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw 0 1 2762.47 641.02 1123.58 2322.68 2744.44 3169.45 4876.37 ▁▆▇▂▁
CREDSotherEnergy_kgco2e2011_pdw 0 1 117.63 166.94 0.00 40.87 64.42 112.06 1151.73 ▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw 0 1 2880.09 584.12 1506.15 2476.45 2813.93 3235.29 5031.52 ▂▇▅▂▁
CREDScar_kgco2e2018_pdw 0 1 1848.64 566.47 613.65 1413.59 1851.17 2258.37 3546.98 ▃▆▇▃▁
CREDSvan_kgco2e2018_pdw 0 1 266.07 335.98 22.61 130.24 194.83 265.58 2801.60 ▇▁▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw 0 1 2114.71 684.48 760.81 1607.59 2131.50 2558.57 4223.23 ▅▇▇▂▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level all per dwelling emissions against IMD score

Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -9.9011, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7213850 -0.5262861
## sample estimates:
##        cor 
## -0.6338111
##    LSOA11CD            WD18NM          All_Tco2e_per_dw
##  Length:148         Length:148         Min.   : 5.846  
##  Class :character   Class :character   1st Qu.:11.906  
##  Mode  :character   Mode  :character   Median :15.465  
##                                        Mean   :17.127  
##                                        3rd Qu.:21.051  
##                                        Max.   :44.958
##     LSOA11CD     WD18NM All_Tco2e_per_dw
## 1: E01017249    Shirley         44.95842
## 2: E01017148    Bassett         43.54419
## 3: E01017197 Freemantle         41.42910
## 4: E01017224   Peartree         31.22609
## 5: E01017180    Coxford         30.70376
## 6: E01017214  Millbrook         30.16370
##     LSOA11CD    WD18NM All_Tco2e_per_dw
## 1: E01017245 Redbridge         7.967564
## 2: E01017241 Redbridge         7.871967
## 3: E01032738    Bevois         7.870684
## 4: E01017182   Coxford         7.344557
## 5: E01017139   Bargate         7.015385
## 6: E01017140   Bargate         5.845638

4.2.1.2 Home energy use

Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.

## Per dwelling T CO2e - gas emissions
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.82 1358.01 1727.03 1758.06 2166.35 3659.41
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level gas per dwelling emissions against IMD score

Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -7.7513, df = 146, p-value = 1.421e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6450947 -0.4147371
## sample estimates:
##      cor 
## -0.53995

Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.1523, df = 146, p-value = 0.03301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32744768 -0.01443342
## sample estimates:
##        cor 
## -0.1753689

Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.1523, df = 146, p-value = 0.03301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32744768 -0.01443342
## sample estimates:
##        cor 
## -0.1753689
##                  RUC11 mean_gas_kgco2e mean_elec_kgco2e mean_other_energy_kgco2e
## 1: Urban city and town        1758.058         1004.407                 117.6261

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 17.213, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7571077 0.8655189
## sample estimates:
##       cor 
## 0.8184714
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 16.017, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7311467 0.8501570
## sample estimates:
##       cor 
## 0.7983163
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

How does the correlation look now?

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level car use per dwelling emissions against IMD score

Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -5.833, df = 146, p-value = 3.37e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5570107 -0.2940157
## sample estimates:
##        cor 
## -0.4347367
##                  RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Urban city and town        1848.645        266.0683

Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level van use per dwelling emissions against IMD score

Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.59071, df = 146, p-value = 0.5556
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1134079  0.2085304
## sample estimates:
##        cor 
## 0.04882944

4.2.2 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   211.0   341.8   409.0   466.5   548.2  1140.0
## N elec meters
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   430.0   631.5   695.5   733.8   800.8  1392.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.3 Estimating the annual emissions levy

Case studies:

  • Annual carbon tax
  • Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

EU carbon ‘price’

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

  • based on a Marginal Abatement Cost (MAC)
  • 2021:
    • Low: £122/T
    • Central: £245/T <- use the central value for now
    • High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.3.1 Scenario 1: Central cost

The table below shows the overall £ GBP total for the case study area in £M.

## £m total
##    nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:    148        435.3365         44.77884             26.66072
## £m by regions covered
##        region nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: South East    148        435.3365         44.77884             26.66072

The table below shows the mean per dwelling value rounded to the nearest £10.

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1:                  4200                       430                        250
##    beis_GBPtotal_c_energy_perdw
## 1:                          680

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 4.7: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 4.8: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1432    2917    3789    4196    5157   11015
##     LSOA11CD         LSOA01NM     WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01017249 Southampton 011C    Shirley              44958.42             11014.812
## 2: E01017148 Southampton 001D    Bassett              43544.19             10668.326
## 3: E01017197 Southampton 020E Freemantle              41429.10             10150.129
## 4: E01017224 Southampton 024C   Peartree              31226.09              7650.391
## 5: E01017180 Southampton 002A    Coxford              30703.76              7522.422
## 6: E01017214 Southampton 019C  Millbrook              30163.70              7390.107
##     LSOA11CD         LSOA01NM    WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01017245 Southampton 012E Redbridge              7967.564              1952.053
## 2: E01017241 Southampton 007B Redbridge              7871.967              1928.632
## 3: E01032738 Southampton 022F    Bevois              7870.684              1928.318
## 4: E01017182 Southampton 004A   Coxford              7344.557              1799.416
## 5: E01017139 Southampton 029A   Bargate              7015.385              1718.769
## 6: E01017140 Southampton 023D   Bargate              5845.638              1432.181

Figure ?? repeats the analysis but just for gas.

Anything unusual?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3.14  332.71  423.12  430.72  530.76  896.55
##     LSOA11CD         LSOA01NM     WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01017249 Southampton 011C    Shirley     3.659406                  896.5545
## 2: E01017148 Southampton 001D    Bassett     3.633488                  890.2047
## 3: E01017197 Southampton 020E Freemantle     2.998158                  734.5488
## 4: E01032753 Southampton 009F  Portswood     2.945786                  721.7175
## 5: E01017252 Southampton 011D    Shirley     2.924247                  716.4405
## 6: E01017145 Southampton 001B    Bassett     2.903698                  711.4061
##     LSOA11CD         LSOA01NM   WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01017142 Southampton 029C  Bargate    0.6995069                171.379188
## 2: E01032748 Southampton 029G  Bargate    0.6532194                160.038752
## 3: E01017140 Southampton 023D  Bargate    0.5874720                143.930649
## 4: E01017281 Southampton 032D Woolston    0.3330864                 81.606173
## 5: E01032755 Southampton 029I  Bargate    0.2409302                 59.027907
## 6: E01032746 Southampton 029F  Bargate    0.0128181                  3.140433

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   160.6   230.0   242.3   246.1   255.8   357.7
##     LSOA11CD         LSOA01NM     WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01032746 Southampton 029F    Bargate      1.459952                   357.6883
## 2: E01017202 Southampton 016C  Harefield      1.299459                   318.3676
## 3: E01017270 Southampton 003C Swaythling      1.284754                   314.7646
## 4: E01017170 Southampton 027D   Bitterne      1.265758                   310.1107
## 5: E01032748 Southampton 029G    Bargate      1.265183                   309.9698
## 6: E01017142 Southampton 029C    Bargate      1.262154                   309.2277
##     LSOA11CD         LSOA01NM     WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01017138 Southampton 023C    Bargate     0.8657028                   212.0972
## 2: E01017160 Southampton 017D     Bevois     0.8092219                   198.2594
## 3: E01017281 Southampton 032D   Woolston     0.7904938                   193.6710
## 4: E01017250 Southampton 010B    Shirley     0.7889987                   193.3047
## 5: E01017196 Southampton 020D Freemantle     0.7812467                   191.4054
## 6: E01017278 Southampton 031D   Woolston     0.6554957                   160.5964

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   275.3   569.1   672.4   676.8   776.5  1194.7

4.2.3.2 Scenario 2: Rising block tariff

Applied to per dwelling values (not LSOA total) - may be methodologically dubious?

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw
##        0%       25%       50%       75%      100% 
##  5845.638 11905.745 15464.936 21050.667 44958.416
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##            V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw
##  1: 14.056160                 1452.5009                  526.8518                     0.000
##  2: 18.324152                 1452.5009                  872.0019                  1049.332
##  3:  9.203213                 1122.7920                    0.0000                     0.000
##  4:  7.015385                  855.8769                    0.0000                     0.000
##  5:  5.845638                  713.1678                    0.0000                     0.000
##  6: 14.007034                 1452.5009                  514.8159                     0.000
##  7: 26.572009                 1452.5009                  872.0019                  4076.296
##  8: 25.334282                 1452.5009                  872.0019                  3622.050
##  9: 21.013503                 1452.5009                  872.0019                  2036.324
## 10: 25.055866                 1452.5009                  872.0019                  3519.871
##     beis_GBPtotal_sc2_perdw
##  1:               1979.3527
##  2:               3373.8348
##  3:               1122.7920
##  4:                855.8769
##  5:                713.1678
##  6:               1967.3167
##  7:               6400.7985
##  8:               5946.5525
##  9:               4360.8268
## 10:               5844.3740
Table 4.2: Data summary
Name …[]
Number of rows 148
Number of columns 3
Key NULL
_______________________
Column type frequency:
numeric 3
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
V1 0 1 17.13 7.01 5.85 11.91 15.46 21.05 44.96 ▆▇▃▁▁
beis_GBPtotal_sc2_perdw 0 1 3221.47 2302.13 713.17 1452.80 2329.22 4374.47 13148.61 ▇▃▂▁▁
beis_GBPtotal_sc2 0 1 2184016.07 1266099.70 650296.60 1113364.33 1881431.98 2831522.94 6640047.94 ▇▅▂▁▁
##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:    148      435.3365      323.2344

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:               1900.7450               165.67756
## 2:               2388.2635               165.67756
## 3:               1032.2892               125.93928
## 4:                870.0000               106.14000
## 5:                587.4720                71.67159
## 6:                699.5069                85.33984
##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1:               1900.7450               165.67756                90.40898
## 2:               2388.2635               165.67756                90.40898
## 3:               1032.2892               125.93928                 0.00000
## 4:                870.0000               106.14000                 0.00000
## 5:                587.4720                71.67159                 0.00000
## 6:                699.5069                85.33984                 0.00000
##    beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1:                63.75374             319.84029
## 2:               242.67303             498.75957
## 3:                 0.00000             125.93928
## 4:                 0.00000             106.14000
## 5:                 0.00000              71.67159
## 6:                 0.00000              85.33984
## [1] 31.10013

## [1] 16.02575

## £m total
##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1:    148                323.2344            31.10013             16.02575 252760
## £m total by regions covered
##        region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: South East    148                323.2344            31.10013             16.02575 252760

4.2.4 Estimate retofit costs

  • from A-E <- £13,300
  • from F-G <- £26,800

Source: English Housing Survey 2018 Energy Report

Model excludes EPC A, B & C (assumes no need to upgrade)

Adding these back in would increase the cost… obvs

## To retrofit D-E (£m)
## [1] 761.6416
## Number of dwellings: 57266
## To retrofit F-G (£m)
## [1] 146.4769
## Number of dwellings: 5466
## To retrofit D-G (£m)
## [1] 908.1185
## To retrofit D-G (mean per dwelling)
## [1] 14417.74
##    meanPerLSOA_GBPm total_GBPm
## 1:         6.135936   908.1185
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.5.2 Scenario 2

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.6 Years to pay…

4.2.6.1 Scenario 1

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.301   2.707   3.745   4.032   4.977  10.307
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.69   18.00   21.43   22.74   25.60   57.64
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Highest retofit sum cost
##      LSOA11CD         LSOA11NM     WD18NM retrofitSum yearsToPay  epc_D_pc  epc_E_pc
##  1: E01017154 Southampton 022B     Bevois    14171398   37.90541 0.1505102 0.2806122
##  2: E01017202 Southampton 016C  Harefield    11080907   31.66890 0.2780847 0.2412523
##  3: E01017192 Southampton 021A Freemantle    10179160   22.14927 0.4774775 0.1819820
##  4: E01017185 Southampton 002D    Coxford     9724814   22.94021 0.4194260 0.2163355
##  5: E01017260 Southampton 026D    Sholing     9723096   24.72392 0.4069149 0.2287234
##  6: E01032753 Southampton 009F  Portswood     9423102   14.51705 0.4514925 0.2238806
##  7: E01017219 Southampton 028B   Peartree     8713800   26.96406 0.3356048 0.1294719
##  8: E01017256 Southampton 026A    Sholing     8611899   16.75621 0.5176471 0.1717647
##  9: E01017151 Southampton 006C    Bassett     8479424   21.83436 0.4897436 0.2410256
## 10: E01017257 Southampton 026B    Sholing     8436594   27.12304 0.4007220 0.1407942
##        epc_F_pc    epc_G_pc
##  1: 0.191326531 0.122448980
##  2: 0.211786372 0.062615101
##  3: 0.072072072 0.016216216
##  4: 0.136865342 0.017660044
##  5: 0.178191489 0.039893617
##  6: 0.059701493 0.005597015
##  7: 0.097103918 0.013628620
##  8: 0.007058824 0.004705882
##  9: 0.056410256 0.000000000
## 10: 0.102888087 0.009025271

What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…

4.2.6.2 Scenario 2

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.090   3.216   6.080   6.824   9.741  20.699
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.69   18.00   21.43   22.74   25.60   57.64
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.3 Compare scenarios

Comparing pay-back times for the two scenarios - who does the rising block tariff help?

x = y line shown for clarity

5 R environment

5.1 R packages used

  • base R (R Core Team 2016)
  • bookdown (Xie 2016a)
  • data.table (Dowle et al. 2015)
  • ggplot2 (Wickham 2009)
  • kableExtra (Zhu 2018)
  • knitr (Xie 2016b)
  • rmarkdown (Allaire et al. 2018)
  • skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.
Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.
Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.
Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.